Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

摘要: As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated ML in wireless communication. This paper comprehensively surveys recent advances communication, which are classified as: resource management MAC layer, networking mobility network localization application layer. The further include power control, spectrum management, backhaul cache beamformer design computation while based focuses on clustering, base station switching user association routing. Moreover, literatures each aspect organized according adopted techniques. In addition, several conditions applying communication identified help readers decide whether use kind techniques use, traditional approaches also summarized together with their performance comparison approaches, motivations surveyed adopt clarified. Given extensiveness area, challenges unresolved issues presented facilitate future studies, where slicing, infrastructure update support paradigms, open data sets platforms researchers, theoretical guidance implementation so discussed.